Public wireless network performance management system with mobile device data collection agents

ABSTRACT

A wireless network performance management system and method. The system includes at least one collection agent for collecting data related to at least one of service coverage; service quality; and usage of public and/or private data networks for enterprise clients, and a reporting unit to graphically represent the collected data to at least one of track, troubleshoot, and analyze the one of the service coverage; the service quality; and the usage of public and/or private data networks for the enterprise clients.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a Continuation of U.S. application Ser. No.13/017,751 filed Jan. 31, 2011 and claims the benefit of U.S.Provisional Patent Application No. 61/300,291 filed on Feb. 1, 2010. Thedisclosures of both above-noted documents are expressly incorporated byreference herein in their entireties.

BACKGROUND OF THE INVENTION 1. Filed of the Invention

The present invention relates to the field of wireless communications.More particularly, the present invention relates to the combinedpractices of Network Performance Management and Mobile DeviceManagement. Even more particularly, the present invention relates to oneor more mobile hosts monitoring one or more data measurements of one ormore public wireless networks from one or more terminal nodes of anetwork, locally storing the collected data, processing the data throughan artificial intelligence engine, and periodically communicating thecollected data back to a centralized data collection server where it mayagain be processed through an artificial intelligence engine and storedinto a database so it can be viewed with a graphical and analyticalfront-end user interface.

2. Discussion of Background Information

Within the last two decades, wireless networks and the surroundingecosystem of mobile computing products have been steadily gaining inmarket adoption. The promises of wireless adoption include high returnon investment, increased mobile worker productivity, ubiquitous publicwide area networks, high network speed, and high network security. Inmany cases, these promises have been realized. However, in many othercases, the value gained from wireless adoption has fallen short ofexpectations.

For many enterprises, deployment of mobile solutions and adoption ofpublic wireless networks have included problems such as unexpectedoverages and fees, dropped calls, lost connections, intermittentcoverage, lower than anticipated bandwidth per mobile worker, andvariations in network trust. In addition, current trends in publicwireless network supply versus demand are expected to drive theelimination of unlimited pricing plans in favor of tiered pricing planswith specified usage limits. These trends will serve to exacerbate thepain currently felt by enterprises trying to manage and control expensesrelated to their mobile workforce. In addition, with the increasedadoption of wireless networks and an increasingly mobile workforce, aswell as trends in mobile broadband technology development, enterpriseshave found that mobile assets are fundamentally more difficult to managethan fixed assets.

Historically, enterprises have turned to network performance managementtools to help control the problems listed above. Unfortunately, mostexisting products in the marketplace were designed for wired networksand for wireless networks that are fully controlled by the enterprise(i.e. private WiFi among others).

Most of the existing products in the marketplace gather performance dataon the networks using data collection agents in the networkinfrastructure (i.e. routers, switches, among others). When theinfrastructure is inaccessible to the enterprise, because the network ispublic, these tools do not work. In addition, many of the existingproducts in the marketplace communicate collected data back to a centralserver using standard protocols such as Simple Network ManagementProtocol (SNMP) Netflow. While these protocols work well on traditionalwired networks, they are chatty, inefficient, and result in inflatednetwork usage costs when used on public wireless networks.

In addition standard systems developed for wired networks rely on snapshots of data being available to build historical knowledge of how asystems state varies over time. For example, Simple Network ManagementProtocol (SNMP) will continually poll a device for network statisticstaking a temporal snap shot of the state of the Transmission ControlProtocol (TCP) stack at the instant of each poll. This snapshot of datais then stored on a server for future analysis. These standards basedmanagement systems have gaps in knowledge created by intermittentconnectivity when running over wireless networks due to regions of lowsignal and connectivity errors. If a mobile device is unable to connectwhen a sample is requested by an SNMP management system the mobiledevice's state at that instant and location is lost forever and cannotbe used for future analysis.

Another example demonstrating the limitations in the current state ofthe art for network management systems is RFC 3954 Cisco Systems NetFlowServices Export Version 9, and in particular to section “3.3—TransportProtocol.” The disclosure of RFC 3954 is expressly incorporated byreference herein in its entirety. The system is designed without regardfor congestion—let alone intermittent connectivity. RFC 3954 recommendsa dedicated link from the data collection agent to the serverspecifically to avoid solving the congestion or intermittentconnectivity problems. This type of system obviously cannot allow anenterprise to manage their use of public wireless networks,

Also, the performance characteristics of wireless networks are uniquefrom wired networks in that they vary over space and time. Two points,separated by space, can and often will experience differing levels ofnetwork quality on a wireless network. Further, measurements of networkquality on a wireless network for a single point in space but withmeasurements separated in time often vary as well. Traditional networkperformance management systems do not collect Geographical/GeospatialInformation System (GIS) location as the data collection agents aredeployed to network infrastructure hosts that are fixed in space.Traditional systems do not account for network measurements collectedover time and correlated to a dynamic GIS location.

Therefore, a need exists for enterprises to collect data about devicesusing public wireless networks and the network performance that thedevice experienced over time correlated to device GIS location so thatenterprises can determine problematic devices and so that enterprisescan determine problematic areas of the public wireless network.Additionally a method is needed to maintain the historical knowledge ofhow a device's state (location, packet counts, signal strength, runningapplications, processes, errors etc) changes over time even when thedevice is in areas of poor signal strength preventing a good connectionor simply is intermittently connected to a network so that historicaltrends can be monitored without loss of information. Additionally, aneed exists to collect data about devices using public wireless networksand their network usage levels over time and location so thatenterprises can control costs associated with excess usage or costsassociated with under-used devices. Additionally, a need exists tocollect data about highly mobile devices equipment inventories and usagepatterns so that enterprises can better manage mobile assets.Additionally, a need exists to minimize bandwidth requirements oftransmitting collected data to a central server so as to minimize usagecost overhead of doing so. Additionally, a need exists to facilitateanalysis of the collected data to ease the burden of the above mentionedproblems by making all collected data accessible in a graphical frontend repotting system that provides GIS map and chart basedvisualizations of the correlations among the collected data.

SUMMARY OF THE INVENTION

in view of the foregoing, an aspect of the present invention is directedto provide for a network performance management system with datacollection agents in the terminal nodes of the network.

Embodiments are directed to a method for managing performance in acommunications environment comprising multiple different networks thatinclude public and/or private data networks. The method includesreceiving from a plurality of mobile devices arranged as networkterminal nodes located at respective current locations within thecommunications environment, data specific to each network interface ofeach of the plurality of mobile device, which was simultaneouslycollected by each of the plurality of mobile device at the respectivecurrent locations, the data being related to at least one of servicecoverage; service quality; and usage of the public and/or private datanetworks for enterprise clients; and graphically displaying thecollected data from the multiple different networks to at least one oftrack, troubleshoot, and analyze the one of the service coverage; theservice quality; and the usage of the public and/or private datanetworks for the enterprise clients at the respective current locations.

Embodiments of the invention are directed to a wireless networkperformance management system that includes at least one collectionagent for collecting data related to at least one of service coverage;service quality; and usage of public and/or private data networks forenterprise clients; and a reporting unit to graphically represent thecollected data to at least one of track, troubleshoot, and analyze theone of the service coverage; the service quality; and the usage ofpublic and/or private data networks for the enterprise clients.

According to an aspect of the present invention, data collection agentsreside on Mobile Devices that represent the terminal nodes of thenetwork.

According to another aspect of the present invention, data collectionagents are capable of dynamically discovering active network interfacesfor wireless networks accessible to the Mobile Device.

According to another aspect of the present invention, data collectionagents are capable of dynamically discovering active GPS interfaces onthe Mobile Device.

According to another aspect of the present invention, data collectionagents are capable of collecting data against multiple networkssimultaneously with the multiple interfaces to similar networks(bandwidth aggregation) or to dissimilar networks (roaming).

According to another aspect of the present invention, data collectionagents are capable of continuing to collect data even when the MobileDevice is not connected to a network or is only intermittently connectedto a network.

According to another aspect of the present invention, data collectionagents are resilient to network unreliability and congestion through theuse of persistent buffering on the Mobile Device on which the datacollection agent resides.

According to another aspect of the present invention, data is collectedthat pertains to the Mobile Device including device name, devicemanufacturer, operating system version, and logged in user name, amongothers.

According to another aspect of the present invention, data is collectedthat pertains to the applications and processes running on a MobileDevice including start. times, end times, process ids, executable names,network flows created by the process, security contexts, protocols used,ports used, interfaces used, and IP addresses, among others.

According to another aspect of the present invention, data is collectedthat pertains to specific network interface devices and the activityoccurring on each including name, manufacture, hardware version,firmware version, driver version, phone number, maximum technologycapability, technology used, home carrier, active carrier, cell towerID, signal strength, transport layer retries, MTU sizes, packet loss,latency and jitter, and efficiency, among others.

According to another aspect of the present invention, location of amobile device over time is collected.

According to another aspect of the present invention, all othercollected data is correlated to both time and the location of the mobiledevice.

According to another aspect of the present invention, data collectionagents are capable of varying the rate of data collection in relation tothe velocity of the mobile device for the purpose of achieving a moreuniform geographic data distribution

According to another aspect of the present invention, data collectionagents are capable of compressing data element values over time, such assignal strength among others, using the Douglas-Peucker reductionalgorithm.

According to another aspect of the present invention, an anonymousreporting mode is provided by which all information that could be usedto identify a user are removed from the data collection and reporting.This would include but is not limited to device name, user name, phonenumber, location etc.

According to another aspect of the present invention, an artificialintelligence engine is provided that is capable of monitoringenvironmental conditions, data collection instant values and datacollection trends, evaluating the monitored values against configuredrules, and triggering certain actions when the evaluated rules indicateto do so.

According to another aspect of the present invention, an artificialintelligence engine can operate on the Mobile Device, on the Server, oron both.

According to another aspect of the present invention, a method isprovided to configure artificial intelligence engine rules with billingperiod time-ranges, usage limits, and notification email address so asto provide automatic email warnings when usage limits are projected tobe exceeded.

According to another aspect of the present invention, a method isprovided to configure artificial intelligence engine rules with billingperiod time-ranges and usage limits so as to provide automatic disablingof network interfaces to prevent usage and cost overages.

According to another aspect of the present invention, a method isprovided to configure artificial intelligence engine rules to inform theuser that is in a region of poor coverage the nearest region of goodnetwork coverage so the user can relocate for the purpose of continuingnetwork communications.

According to another aspect of the present invention, a method isprovided for a graphical reporting user interface that provides variousreports based on the data collected by the data collection agents.

According to another aspect of the present invention, a report isprovided that correlates device characteristics with networkcharacteristics over time and location for the purpose of auditingpublic network billing statements, identifying and managing over-used,under-used, and problematic devices, and to troubleshoot performanceproblems occurring in the networks.

According to another aspect of the present invention, a report isprovided on the applications and processes in use on a Mobile Device.

According to another aspect of the present invention, a report isprovided on the percentage of total network usage caused by specificapplications, processes, and users.

According to another aspect of the present invention, a report isprovided on application transaction time as they vary over time,location, cell tower, carrier, phone number, modem manufacture, devicemanufacture, device driver version etc to identify reasons forvariations in performance.

According to another aspect of the present invention, a report isprovided on application performance such as application bytes sent andreceived as they vary over time, location, cell tower, carrier, phonenumber, modem manufacture, device manufacture, device driver version etcto identify reasons for variations in performance.

According to another aspect of the present invention, a report isprovided on the security account used to launch applications andprocesses.

According to another aspect of the present invention, a report isprovided on the list of flows created by applications and processes.

According to another aspect of the present invention, a report isprovided on what protocols, ports, interfaces, IP addresses, andnetworks are used by specific applications as they vary over time,location, carrier, cell tower.

According to another aspect of the present invention, a report isprovided on all transport layer packets and tracking the state of eachTCP connection including protocol state, window size, TCP options,timestamp options, selective acknowledgment (SACK) metrics, minimum,maximum, average, and standard deviation of round trip times, retries,total bytes sent and received as they vary over time, location, celltower, carrier, phone number, modem manufacture, device manufacture,device driver version etc to identify reasons for variations inperformance.

According to another aspect of the present invention, a report isprovided tracing a device route over time overlaid on top of mappingsoftware while indicating the variations in signal strength, technologytype, error rates, transport layer performance, network layerperformance and application layer performance.

According to another aspect of the present invention, a report isprovided replaying a device route over time overlaid on top of mappingsoftware while indicating the variations in signal strength, technologytype, error rates, transport layer performance, network layerperformance and application layer performance.

According to another aspect of the present invention, a report isprovided that can be configured with billing period time-ranges so as toprovide appropriate usage and cost projections.

According to another aspect of the present invention, a report isprovided that predicts roaming charges as indicated by device totalbytes sent and received while doing international roaming.

According to another aspect of the present invention, a report isprovided that distinguishes between non billable roaming and billableroaming.

According to another aspect of the present invention, a report isprovided that indicates home network and partner networks visited bylocation.

According to another aspect of the present invention, a report isprovided that indicates visited cell towers.

According to another aspect of the present invention, a report isprovided and score cards for comparing performance of different carriersby time, location, modern manufacture, device manufacture, device driverversion, OS types, OS version, protocol type such as IPv4 or IPv6 , andVPN type.

According to another aspect of the present invention, a report isprovided on memory consumption, CPU, semaphores, locks and otheroperating system resources.

According to another aspect of the present invention, a report isprovided showing location usage densities by geographic regions toreport number of users, devices, and byte counts by location.

According to another aspect of the present invention, a report isprovided by protocols, such as the internet protocol version in use,e.g., IPv4 or IPv6, by location, time and carrier.

According to another aspect of the present invention, a report isprovided that will list the nearest N users in the devices network bylocation for the purpose of knowing who is nearby to assist the userwhen needed.

Embodiments of the invention arc directed to a method. that includescollecting data related to at least one of service coverage; servicequality; and usage of public and/or private data networks for enterpriseclients, and graphically displaying the collected data to at least oneof track, troubleshoot, and analyze the one of the service coverage; theservice quality; and the usage of public and/or private data networksfor the enterprise clients.

Other exemplary embodiments and advantages of the present invention maybe ascertained by reviewing the present disclosure and the accompanyingdrawing.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is further described in the detailed descriptionwhich follows, in reference to the noted plurality of drawings by way ofnon-limiting examples of exemplary embodiments of the present invention,in which like reference numerals represent similar parts throughout theseveral views of the drawings, and wherein:

FIG. 1 illustrates a block diagram of the various components of thepresent invention;

FIG. 2 illustrates a block diagram of the data collection agentcomponent of the present invention;

FIG. 3 illustrates, in one exemplary embodiment, a data collection agentmonitoring more than one network simultaneously;

FIG. 4 illustrates an exemplary embodiment of a data collection agentvarying the data collection rate in correlation to the velocity of themobile device;

FIG. 5 illustrates an exemplary embodiment of a data series over timebeing compressed using the Douglas-Peucker reduction algorithm;

FIG. 6 illustrates an exemplary embodiment of a specific data element(RSSI) over time being compressed using the Douglas-Peucker reductionalgorithm;

FIG. 7 illustrates an exemplary configuration file, according to anaspect of the present invention;

FIG. 8 illustrates, in one exemplary embodiment, the correlated dataelements collected by the data collection agents and stored in thecentral data repository;

FIG. 9 illustrates, in one exemplary embodiment, a block diagram of anartificial intelligence engine that evaluates environmental conditions,data collection instant values, and data collection trends;

FIGS. 10A and 10B illustrate an exemplary configuration file forconfiguring the rules that drive the artificial intelligence engine;

FIG. 11 illustrates, in one exemplary embodiment, a table of conditionsfor artificial intelligence rules, with associated business problemsthat the conditions may help to address;

FIG. 12 illustrates, in one exemplary embodiment, a list of predicatesthat can qualify conditions used in artificial intelligence rules;

FIG. 13 illustrates, in one exemplary embodiment, a list of actions thatcan be taken when a configured set of predicates and conditions evaluateto true while the Artificial Intelligence engine processes a rule;

FIG. 14 illustrates, in one exemplary embodiment, a businessintelligence report, derived from the data elements collected by thedata collection agents over time and location that provides insight intoactual data activity and predicted carrier billing levels;

FIG. 15 illustrates, in one exemplary embodiment, a businessintelligence report, derived from the data elements collected by thedata collection agents over time and location that provides insight intothe productivity performance of a population of mobile devices;

FIG. 16 illustrates, in one exemplary embodiment, a businessintelligence report, derived from the data elements collected by thedata collection agents over time and location that provides insight intomanaging the asset inventory of a population of mobile devices;

FIG. 17 illustrates, in one exemplary embodiment, a businessintelligence report, derived from the data elements collected by thedata collection agents over time and location that provides insight intothe performance of wireless networks over space and time; and

FIG. 18 illustrates, in one exemplary embodiment, a businessintelligence report, derived from the data elements collected by thedata collection agents over time and location that provides insight intothe location of a set of Mobile Devices over time.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The particulars shown herein are by way of example and for purposes ofillustrative discussion of the embodiments of the present invention onlyand are presented in the cause of providing what is believed to he themost useful and readily understood description of the principles andconceptual aspects of the present invention. In this regard, no attemptis made to show structural details of the present invention in moredetail than is necessary for the fundamental understanding of thepresent invention, the description taken with the drawings makingapparent to those skilled in the art how the several forms of thepresent invention may be embodied in practice.

The present invention is a distributed network performance managementsystem. As such, it is composed of data collection agents located on theterminal nodes of the system and a central server comprised of a webserver, a database, and an artificial intelligence engine 8. FIG. 1shows an overall system component diagram with Mobile Devices 1 on whichthe data collection agents reside. Wireless Networks 4, 5 of whichmultiple networks may he simultaneously in use, a Web Server 6 that bothreceives collected data and provides access to the reports based on thatdata, a Database 7 that provides the historical data storage for thesystem, and an Artificial Intelligence Engine 8 that receives incomingdata from the Mobile Devices 1 and reads historical data from theDatabase 7 to evaluate rules and take actions as warranted.

By way of example, Mobile Devices 1 can include laptops, netbooks,smartphones, handheld devices, workstations, PDAs, IPADs, tabletcomputers, etc. Further, wireless networks 4, 5 can include WiFi,cellular networks technologies such as WiMax, 3G, 4G and Long TermEvolution (LTE), as well as other radio networks. Web server 6 caninclude Internet Information Service (IIS), APACHE TOMCAT, ORACLE HTTPServer and others. Database 7 can include MySQL, SQL Server, dBASE,MICROSOFT ACCESS, etc. Artificial intelligence Engine 8 can includeautomatic system generated notifications and alerts, policy enforcement,conditional system responses based on the nature and content ofcollected information.

The primary advantage to a distributed network performance managementsystem with data collection agents located on the terminal nodes of thenetwork is the ability to perform measurements on public networks thatare neither owned nor controlled by the party operating the performancemanagement system. The data collection agents can include softwaredrivers, software agents, firmware, or embedded hardware (orcombinations thereof) in the mobile devices. In addition, since theMobile Devices 1, which make up most of the terminal nodes of publicwireless networks, often make use of multiple public wireless networks,the current invention is capable of monitoring the performance ofmultiple networks simultaneously. Further, the Mobile Devices 1 usingpublic wireless networks are generally moving through space therebymaking location a data measurement that, when correlated to othernetwork performance measures, offers unique advantages to enterpriseswishing to monitor the performance of their mobile workforce.

Terminal nodes of the mobile network are where the data collectionagents are located. FIG. 2 shows a block diagram of an exemplaryembodiment of a data collection agent 9 in the present invention. InFIG. 2, the Controller 10 coordinates the actions of all of the otherentities in Data Collection agent 9, which as noted above, can be formedor implemented in hardware, software and/or firmware or as combinationsthereof. The Controller 10 is the entity that receives and processescommands from the User Interface 18. By way of non-limiting example, theUser Interface 18 may be a graphical user interface on a WINDOWSmachine, LINUX machine, MAC OS machine, text file, web interface orserial port, or the User Interface 18 may be a configuration file storedin a database in Mobile Device 1 or encrypted in a secure file andaccessible from locations such as a secure server, another device, or aweb-based interface. By way of non-limiting example, the User Interface18 may send commands that get or set configuration, start or stop thedata collection agent, view the current operational status of thevarious data collectors (network, system, location) and their mostrecently collected data, and retrieve debugging information among otherpossible commands.

In FIG. 2, the Controller 10 creates and controls the Collector Manager11, the Data Storage Manager 14, the. Transmission Manager 12, and theAdapter Manager 13.

The Collector Manager 11 is responsible for controlling each of theindividual collectors including the Location Collector 17, the SystemCollector 15, and each of the Network Collectors 16. The CollectorManager 11 creates both the Location Data Collector 17 and the SystemCollector 15, which run for the lifetime of data collection agent 9. Anetwork Collector 16 is created by the Adapter Manager 13 for eachnetwork interface created for a detected available network for MobileDevice 1 and then provided to the Collector Manager 11. The CollectorManager 11 is responsible for periodically retrieving collected datafrom each of the collectors, formatting that data, and storing it in theData Storage Manager 14.

The System Collector 15 is responsible for collecting all system datafrom the Mobile Device 1. System data includes all data that is notspecific to either location of the Mobile Device 1 or to a particularnetwork interface. By way of non-limiting example, the System Collector15 may collect information pertaining to the applications and processesrunning on a Mobile Device 1, network connection and flows associatedwith the applications and processes running on a Mobile Device 1, thesecurity principal associated. with the applications and presses runningon a Mobile Device 1, the network transaction time associated with flowsthat are associated with the applications and processes running on aMobile Device 1, the protocol state, window size, TCP options, timestampoptions, SACK metrics, minimum, maximum, average, and standard deviationof round trip times, retries, total bytes sent and received, theprotocol type (such as, e.g., IPv4 car IPv6 or future generationsthereof) for flows associated with applications or processes running onthe Mobile Device 1, the OS type and version running on the MobileDevice 1, the virtual private network (VPN) type and version running onthe Mobile Device 1, and the memory consumption, CPU, semaphores, locksand other operating system resources that are available and in-use onthe Mobile Device 1.

Most modern operating systems, e.g., WINDOWS, ANDROID, LINUX, IOS, OSXas well as embedded Reduced Instruction Set Computing (RISC) systemsused in automobiles, appliances and interactive telematics services suchas ONSTAR and FLEETMATICS, could be used to access the information thatthe System Collector 15 provides. For example, the WINDOWS operatingsystems provide full support and documentation for the WINDOWS FilteringPlatform. This platform can be used to acquire much of the informationrequired by the System Collector 17. Similar frameworks exist on othermodern operating systems.

The Location Collector 17 is responsible for finding a globalpositioning system (GPS) device embedded within or attached to themobile device by scanning all available serial ports. Then, using serialport sharing technology, well-known by those practiced in the arts usingsuch standard knowledge as exists in products offered by ELTIMA andFABULATECH, the Location Collector 17 will retrieve incoming GPS dataover the serial port and report it for correlation with all other datareturned by all other collectors. This position information is reportedto Web Server 6 for insertion into Database 7 for display and/or furtheranalysis. The position information can also be forwarded to ArtificialIntelligence Engine 8 for analysis and reporting additional information& conclusions, and taking action to report and inform potential users ofthis information. The manner of correlating the UPS and other data is bytime and device, along with other device-specific and network-specificinformation that is collected.

The Network Collector 16 is responsible for collecting data specific toparticular network interface. While the exemplary embodiments of theinvention are directed to wide area networks, it is understood thatother type networks can also be utilized without departing from thespirit and scope of the embodiments of the invention. In this regard, itis understood that the Network Collector concept described in thisapplication could be expanded by those ordinarily skilled in the art toinclude other network types, such as, by way of non-limiting example,LAN and WLAN networks, cellular networks, satellite networks, WiFi,WiMax, etc. without departing from the spirit and scope of theembodiments, Often. the Network Collector will make use of SoftwareDeveloper Kit (SDK) libraries, provided by networking devicemanufacturers, to access the information it requires. Sometimes however,the information is available through standard devices in the platformoperating system. One example of a standard devices provided by theoperating system is the example given above about the Windows FilteringPlatform. Given a choice of equally accurate data, embodiments of thepresent invention may preferably use standard mechanisms in the platformoperating system. Alternatively, advantageous results can also beachieved through the use of vendor SDK's. The Network Collector 16collects information such as cell tower, phone number, modemmanufacture, device manufacture, device driver version, firmwareversion, maximum technology capability, active technology type, roamingstatus, home network carrier, active network carrier, signal strength,transport. layer retries, MTU sizes, packet loss, latency, jitter,efficiency, as well as bytes and packets sent and received over thenetwork by the Mobile Device 1. Then the Data Storage Manager 14 and theTransmission Manager 12 stores, and transmits the information atspecified intervals. As noted above, network collector 16 is created foreach network available to mobile device 1. In an exemplary embodiment,Network Collector 16 can be, e.g., a WAN Collector that collects datafor an available WAN network. Additionally or alternatively, a NetworkCollector 16 can also be created as a Win Collector and/or anotherMobile Network Technology Collector providing similar functionality forthose network types and/or other WAN Collectors for other available WANnetworks.

For some user groups, the amount of information collected may beconsidered too much. Specifically, such user groups may have privacyconcerns with the amount of data collected. For such user groups, thepresent invention provides for “Anonymous Mode” data collection. In thismode of data collection, the collection of all data that could be usedto identify the user of the Mobile Device 1 that is collecting the datacan be disabled. By way of non-limiting example, such data may includeusername, machine name VPN IP Address, and location, among others. Inmany cases, these user groups may want more control over when “AnonymousMode” is enabled. For these user groups, “Anonymous Mode” may beenabled/disabled for individual devices or for groups of devices basedon time-of-day ranges, day-of-week ranges, and days-of-the-month ranges.

The Data Storage Manager 14 is responsible for maintaining a fast,persistent, and low-overhead data queue. It receives and storescollected data from the Collector Manager 11. The data is stored in aFIFO (first-in-first-out) or “queue” format. When requested by theTransmission Manager 12, the Data Storage Manager 14 provides storeddata to the Transmission Manager 12 so that the data can be sent to theWeb Server 6. The Data Storage Manager 14 provides this information in atransactional manner, If the Transmission Manager 12, fails tosuccessfully send the data to the Web Server 6, then the stored datawon't be removed from the Data Storage Manager 14. But if theTransmission Manager 12 does successfully send the data to the WebServer 6, then the stored data will be removed from the Data StorageManager 14. In addition, the Data Storage Manager 14 will maintainlimits on the amount of data it will store. In one exemplary embodiment,the limits on data storage capacity are configurable. There are variousways in which the storage limit may be enforced including, by way ofnon-limiting example discarding the newest collected data or discardingthe oldest collected data. In one exemplary embodiment, the data discardpolicy is configurable.

The Transmission Manager 12 is responsible for transmitting collecteddata from the Data Storage Manager 14 to the Web Server 6 at appropriatetimes. The Transmission Manager 12 maintains a minimum transmitfrequency. In an exemplary embodiment, the minimum transmit frequency isconfigurable. If there is data to send and the minimum transmitfrequency has elapsed since the last transmission, the TransmissionManager 12 will attempt to send the data to the Web Server 6. The WebServer 6 will return a positive acknowledgement to the TransmissionManager 12 after it has stored all transmitted data thereby ensuring nodata loss. All data sent by the Transmission Manager 12 is compressed bythe Transmission Manager 12 and decompressed by the Web Server 6 usingan industry standard compression algorithm. By way of non-limitingexample, the compression and decompression algorithms may be similar tothat which is described by RFC 1950, the disclosure of which isexpressly incorporated by reference herein in its entirety.

The Adapter Manager 13 is responsible for monitoring the networkinginterfaces that are available on the Mobile Device 1. When the AdapterManager 13 identifies that a network interface has become available, itcreates a new instance of a Network Collector 16 and provides thatinstance to the Collector Manager 11 so that the Collector Manager 11can periodically retrieve collected data from it to be stored andforwarded to both the Data Storage Manager 14 and the ArtificialIntelligence Engine 19. When the Adapter Manager 13 identifies that anexisting network interface has become unavailable, it removes theassociated Network Collector 16 from the Collector Manager 11 anddestroys it.

The Artificial Intelligence Engine 19 is fundamentally the same entityas the Artificial intelligence Engine 8 in FIG. 1. The main differencebetween these two entities is that artificial intelligence engine 8 inFIG. 1 resides on the server and the artificial intelligence engine 19in FIG. 2 resides in the data collection agent 9 in the Mobile Device 1.The Artificial Intelligence Engine 8, 19 is discussed later.

The present invention is capable of monitoring multiple networkssimultaneously by creating multiple Network Collector 16 entities. Inone exemplary embodiment, the present invention is deployed on amultiprocessor Microsoft Server 2008 (R2) platform yielding truesimultaneous use of the networks. FIG. 3 shows, by way of non -limitingexample, two Network Collector 16 entities simultaneously monitoring twonetworks including a Code Division Multiple Access Evolved DataOptimized (CDMA EV-DO) 20 network and a High Speed Downlink PacketAccess (HSDPA) 21 network.

The present invention is capable of continuing to collect datameasurements even when the Mobile Device is not connected to an activenetwork or is only intermittently connected to an active network. Datawill continue to be collected from all available collectors until thedevice is connected to an active network, at which time it will betransmitted.

By way of non-limiting example, on every Mobile Device 1, for eachcollector identified by the Adapter Manager 13, the system, location andnetwork data can be aggregated by Collector Manager 11 and queued inData Storage Manager 14. The queued data can then sent by TransmissionManager 12 at specified intervals to Web Server 6. If Mobile Device 1 isnot connected to an active network at the end of such an interval, thedata can continue to be stored in Data Storage Manager 14 until suchtime as an active network connection is detected. At that time, allaccumulated data may be transmitted to Web Server 6, through either aWireless Network 4, 5, or another network interface, such as a WLAN orLAN connection. The Collection Manager 11 continues to accept data fromeach collector, whether Mobile Device 1 is connected to a network ornot.

While the present invention is not limited to wireless networks andmobile terminal nodes of the network, this environment provides strikingadvantages. However, in a network environment with mobile terminalnodes, some new challenges arise.

One of the unique challenges that arise from the application of thepresent invention to public wireless networks and mobile terminal nodesis the lack of data uniformity that can arise from variations in thevelocity of the mobile terminal nodes.

If a Mobile Device 1 collects data at a constant rate regardless of thevelocity of the Mobile Device 1, then it will collect more data pointsover the same geographical region when it is travelling slowly than whenit is travelling quickly. And in many applications of the collecteddata, a more uniform geographic distribution of the collected data ismore desirable. By way of non-limiting example, if the collected datawere used to generate a coverage map of relative signal strength acrossa geographic region, then a uniform distribution of data would yield amore accurate coverage map than a geographic distribution of data withareas of dense data points where the Mobile Devices 1 were travellingslowly and more sparse data points where the Mobile Devices 1 wheretravelling quickly.

Therefore, the present invention has the capability to vary the datacollection rate in accordance with the velocity of the Mobile Device 1.FIG. 4 shows a Mobile Device 1 travelling for 60 minutes at 20 MPH andcollecting 5 samples during that time. FIG. 4 also shows a Mobile Device1 travelling also for 60 minutes but at a rate of 60 MPH. In this secondexample, 13 samples were taken. In an exemplary embodiment, a variablesampling rate, proportional to vehicle velocity, can be used to producerepresentative samples for a given area regardless of the speed of thedevices traveling through it. Devices that are traveling at higherspeeds would have a higher sampling rate than those moving more slowly.A minimum sampling rate can be used for devices that are stationary orare moving very slowly.

Another unique challenge that arises from the application of the presentinvention to public wireless networks and mobile terminal nodes is theneed to minimize the use of network overhead. The cost of using a publicwireless networks is generally directly related to the amount of datatransmitted over the network. Therefore, a strong need exists tominimize the amount of network bandwidth consumed by a system intendedto monitor the performance of the network.

One method employed by the present invention is to apply theDouglas-Peucker reduction algorithm to many different types of datapoints collected over time.

The Douglas-Peucker reduction algorithm is a polyline simplificationalgorithm. In other words, it smoothes out a line, within a specifiedtolerance level, so that a close approximation of the line can beretained while eliminating many of the individual data points comprisingthat line. Almost all data values collected over time can be graphed ongraph with the X axis being time and the Y axis being the value of thedata. Therefore, the Douglas-Peucker reduction algorithm can be appliedto such a graph maintaining a close approximation of the data value overtime with a vast reduction in the actual collected values over time.

FIG. 5 shows the concept of the Douglas Peucker reduction algorithm andFIG. 6 shows the Douglas-Peucker reduction algorithm applied to thesignal strength data measurement of a wireless network over time.

In Douglas-Peucker reduction, a polyline is processed recursively. Oneach pass, the endpoints of the line are connected and the distancebetween each remaining point on the line and the new line connecting theendpoints is calculated. If the distance of the furthest point is lessthan the specified tolerance value, then the remaining data points arediscarded. Otherwise, the furthest point becomes a new vertex, new linesare drawn from it to the original endpoints, and the distance versustolerance values are recalculated for all remaining points on the newlines. This process continues recursively until all points are withintolerance and have either been eliminated or have been made into avertex.

Referring to FIG 5, graphs 31 through 35 show the evolution of aDouglas-Peucker reduction against a polyline. Graph 31 shows theoriginal polyline with 8 data points. Graph 32 shows the first iterationof the Douglas-Peucker reduction algorithm with a line being drawnbetween the two endpoints, the furthest point from the new lineidentified, and new lines being drawn to the new vertex. Graph 33 showsthe second iteration in which the first segment created in graph 32 iswithin tolerance and the second data point in the line discarded and inwhich the second segment created in graph 32 has a furthest point thatis out of tolerance. So a new vertex is created on the polyline. Graph34 shows the third iteration which results in a new vertex. Graph 35shows the final resulting line which is a close approximation of theoriginal line but with three of the original eight data pointseliminated.

Douglas-Peucker reduction can he applied to any scalar data measurementtaken over time since such measurements can he translated into an XYline graph. By way of non-limiting example, some of the data elementsfor which Douglas-Peucker reduction is applicable include: ReceivedSignal Strength Indication (RSSI), battery remaining, geographiclocation, data xmit/recv rates, error rates, temperature, among others.

Referring to FIG. 6, the application of the Douglas-Peucker reductionalgorithm is demonstrated for the signal strength data measurement thata Mobile Device 1 experiences for a wireless network over time. Line 40shows all of the individual RSSI measurements taken over time. Line 41shows the line resulting from Douglas-Peucker reduction with the arrowsbetween Line 40 and Line 41 showing the points that were retained in theDouglas-Peucker reduction. All of the original data measurements withoutcorresponding arrows between line 40 and line 41 were discarded in theDouglas-Peucker reduction. Finally, line 42 shows the polylinetranslated back into the minimal subset of raw measurements required toreproduce the original trend-line of data measurements with highaccuracy.

Another unique challenge that arises from the environment of the presentinvention is the mitigation of privacy concerns. Since the presentinvention collects data on end-user devices and since the presentinvention collects location information, both the end-users and theenterprise administrators may want to limit the amount and types of datacollected so as to ensure end-user privacy. The present invention hasthe ability to disable the collection of data elements that might leadto end-user identification. Such data elements include location, IPAddress, User Name, and Device Name, among others. By way ofnon-limiting example, the ability to control the collection of thesedata elements may be applied by device identity, geographic location,hour of day, and day of week among others.

Referring to FIG. 2, the User Interface 18 of the data collection agent9 on the Mobile Device 1 may be comprised of a graphical user interfaceoffering the ability to get and set configuration settings that controlthe behavior of the data collection agents. It is also contemplated thatone ordinarily skilled in the art may use a User Interface 18 thatinclude a configuration file. FIG. 7 shows an exemplary embodiment ofsuch a configuration file. In the configuration file, there are settingsfor enabling/disabling the data collection, enabling/disabling anonymousdata collection, enabling/disabling location data collection,enabling/disabling public wireless network data collection, controllingthe data collection rate, controlling the tolerance for theDouglas-Peucker reduction algorithm, controlling local data storagebehavior, controlling access to the GPS devices, and specifying themanner in which to connect to the Web Server 6.

All of the data collection agents on the Mobile Devices 1 periodicallysend all collected data, in compressed form, to the Web Server 6. Theprimary responsibility of the Web Server 6 is to ensure that the data isreliably stored in the Database 7. The Database 7 is the entity in thepresent invention that is responsible for storing all historical datacollected by the data collection agents, correlating that data, andmaking it available to the rest of the system for later retrieval. Inone embodiment, the Database 7 may be implemented as an object orienteddatabase, but can also be implemented as a relational database or anyother type of database. By way of non-limiting example, FIG. 8 shows anexemplary database entity-relationship diagram for a relational databaseimplementation of the Database 7. Referring to FIG. 8, box 50 is anexemplary table where each record describes a Mobile Device 1. As such,it contains columns representing, e.g., Device Name and a uniqueidentifier among others, By way of non-limiting example, the uniqueidentifier may take the form of a universally unique identifier (UUID).Box 51 is the deviceuser table where each record represents a user thathas logged onto a Mobile Device 1. The records in this table containcolumns such as username and a unique identifier among others. Thedevice table 50 and the deviceuser table 51 are both related to thedeviceuserstatus table 52. The deviceuserstatus table is a historicalrecord of each time any deviceuser has logged onto any device. As such,it maintains timestamps as well as references to the device table 50 andthe deviceuser table 51 among other columns. The device table 50 is alsorelated to the devicetype table 54 which serves to categorize records inthe device table 50 into groups of similar hardware and softwareplatforms. By way of non-limiting example, device types may includelaptops, smartphones, handhelds, workstations, among others. The devicetable 50 is also related to the network interface adapter table 55. Eachrecord in the network interface adapter table 55 represents a networkingdevice that may be used by a particular Mobile Device 1 over aparticular period of time and therefore contains columns such astimestamps, references to records from the device table 58, and variouscharacteristics of the networking device such as manufacturer andfirmware version among others. The network interface adapter table 55also relates to the network technology table 57. The network technologytable 57 contains records that describe types of network technologiesused by cards to access public wireless networks. By way of non-limitingexample, network technology types may include HSDPA, CDMA EV-DO, andGPRS among others. The network interface adapter table 55 relates to thenetwork technology table 57 record that represents the highesttechnology type of which the network interface adapter record iscapable. Records in the networksession table 58 represent the discreteperiods of time bounded by the time that a network interface cardconnected to a network and the time that it disconnected from thatnetwork. All network and location measurements taken by a data collectormust, by definition, fall within the bounds of a networksession 58.Related to the networksession 58 records, are thenetworkstatisticslocated 59 records. These records contain statisticalinformation for a discrete sub-period of time within a networksession58. These record types contain references back to the associatednetworksession records, timestamps, network carrier identifiers, andtransmit and receive byte counts, among others. The carrier referencesin these records refer to the carrier table 56. The carrier table 56represents a particular network provider and contains identifyinginformation about that carrier. By way of non-limiting example, thecarrier table 52 may contain a NID and SID value for a CDMA network orit may contain a MCC and MNC value for GSM networks. Thenetworkstatisticslocated 59 table is also related to the gpssegment 60table. Whenever network measurements are collected, any location datathat can be correlated with the collected data are added to thegpssegment 60 table. The network session table 58 is also related to theApplicationStatus table 62. Each record in the ApplicationStatus table62 represents an instance of a running application. As such, each recordcontains a start time and an end time. Each record also contains areference to the Application table 61. The application table 61 containsa record for each unique combination of application name and version inthe system. Also related to the ApplicationStatus 62 table, are theApplicationTcpFlow 63 and ApplicationUdpFlow 64 tables. These tablesrepresent snapshots in time of traffic statistics between two endpointsby a particular application. Therefore, each of these records refers toa record in the ApplicationStatus table 62. Each also contains the IPaddress and port of the remote endpoint as well as byte and packetcounts sent and received between the two endpoints during the timeperiod represented by the current record. Periodically, the database 7must purge its oldest data in order to control maximum resourceconsumption. In one embodiment, this may be performed on-demand by asystem administrator. In an alternate embodiment, the systemadministrator may configure this to occur automatically according to aconfigured schedule.

Both the Mobile Device 1 and the Web Server 6 may contain an ArtificialIntelligence engine 8, 19, respectively, which is described with theblock diagram in FIG. 9. The Artificial Intelligence Engine 8, 19 isconfigured with a series of Rules 84. Rules 84 are composed of one ormore Conditions 82 and one or more Actions 83. Conditions 82 arecomposed of one or more Predicates 89, zero or more Condition Parameters81 and one or more Condition States 80. By way of non-limiting example,the conditions may be evaluated by the Rules Engine 85 applying Rule(s)84 against Instant Values 86 of data measurements. Historical TrendValues 86 of collected data, Environmental Values 88 or a combinationthereof.

When the Rules Engine 85 starts, it first reads all configured rules. Inone exemplary embodiment, the rules may be retrieved over the networkfrom a database or other network service. In another exemplaryembodiment, the rules may be configured in a configuration filecontrolled on either Mobile Device 1 or downloaded to the Mobile Device1 as needed from Server 6. By way of non-limiting example, an exemplaryconfiguration file is shown in FIGS. 10A and 10B depicted in XML format.In FIG. 10A, there are a series of Action 90 elements. Action 90elements describe actions that may be taken when the Conditions 82 andPredicates 89 evaluate to true. By way of non-limiting example, FIG. 10Ashows an Action 90 element that contains an Action of type “SMTP”. Thistype of action causes an email message to be sent. Following the Action90 elements, the configuration file of FIG. 10A shows a series ofPredicate 92 elements. Predicates 92 are used to qualify a Condition 93but are not full Conditions 93 themselves. Next in the file, theConditions 93 are listed. Conditions 93 contain both Configurationparameters and State parameters. Configuration parameters are specifiedwhen the rule is created. State parameters values are set when aCondition 93 is evaluated by the Rules Engine 85 (FIG. 9). after theConditions 93, the configuration file in FIG. 10B lists a series ofRules 94. Rules 94 are comprised of a series of references to Conditions93 and Actions 90. Rules 94 are also configured with Trigger and Resetmessages each of which may contain state or configuration parametervalues from any of the referenced Conditions 93.

Conditions 82 and Predicates 89 represent the evaluation side of therules engine 85. Actions 83 represent what happens after evaluationcompletes. Conditions 82 are primitives in the present invention thatcan accept input parameters and produce output parameters. By way ofnon-limiting example, various examples of Conditions 82 are listed inFIG. 11. The first example describes a condition that evaluates to trueif a Mobile Device 1 has not successfully communicated with the WebServer 6 within a configurable number of minutes. By way of non-limitingexample, the artificial intelligence engine 8 in the data collectionagent 9 on the Mobile Device 1 may use this condition in a rule thatreconfigures the data collection interval on the present invention tocollect data more rapidly. Such a reconfiguration based on real-timefeedback may aid troubleshooting by providing more dense data collectionin problem hot-spots. Also by way of non-limiting example, theartificial intelligence engine 19 on the Web Server 6 may use thiscondition in a rule that sends an SMTP message to a systemadministrator. Other Conditions are described by way of non-limitingexample in FIG. 11 including Conditions 82 to assist withtroubleshooting applications running on the Mobile Device 1, managingover and under utilization of devices and public network usage plans,detecting problems in the performance of the public network, anddetecting usage patterns of company resources.

Sometimes the data set against which conditions are evaluated should belimited. This is the purpose of Predicates 89. Non-limiting examples ofpredicates used in the present invention are listed in FIG. 12. Usingthe example Predicates 89 in FIG. 12, the evaluation of Conditions 82can be limited to specific users, Mobile Devices 1, groups of users andMobile Devices 1, devices using network interfaces with specific phonenumbers, devices operating in a defined geographic area, devices withparticular attributes or using network interface devices with particularattributes, specific days of the week or times of the day, or devicesexperiencing specific operating environments such as a signal strengthabove or below a particular threshold for a particular period of time.

Actions 83 are taken based on the evaluation of Conditions 82 andPredicates 89. Actions 83 are executed when Conditions evaluate to true.Actions 83 may be stateful, meaning that they execute both when theconditions and predicates evaluate to true, and then again when theysubsequently evaluate to false. Such stateful Actions 83, only triggerwhen the evaluation state transitions from true to false or from falseto true.

By way of non-limiting example, FIG. 13 describes some examples of thetypes of Actions 83 that are part of the present invention. Actions 83of type SMTP send email messages. This type of Action 83 is configuredwith the IP Address and Port of the SMTP server as well as the “To” and“From” address, subject line, message, and any additional attachments tothe email message. Actions 83 of type SMTP can also be used to send SMSmessages through the SMS gateways of most major public network carriers.Actions 83 of type SNMP send a trap to a management station and areconfigured with the IP address and port of the management station, thecommunity string, and the OID of the trap. Actions 83 of typeModifySystem are used to modify the configuration of the local operatingenvironment in which the present invention is operating. Actions 83 ofthis type are configured with the key of the setting to change and thevalue to which the setting should be changed. By way of non-limitingexample, this Action 83 may be used to modify the Windows Registry.Actions 83 of type ModifyConfig are used to modify the run-timeconfiguration of the present invention. Actions 83 of this type are alsoconfigured with the key of the setting to change and the value to whichthe setting should be changed. By way of non-limiting example, if acondition evaluated to indicate that a Mobil Device 1 was experiencingnetwork trouble, this Action 83 may be used to increase the datacollection frequency in an effort to gather more data about the problem.Actions 83 of type ToggleRule are used to enable or disable otherartificial intelligence Rules 84 and are simply configured with theidentifier of the Rule 84 to be acted upon. Actions 83 of this type canbe used to create complex chains of rules. Another example of an Action83 type in the present invention is LaunchProcess. Actions 83 of thistype are used to launch additional processes on the local system and areconfigured with the path to the image to launch, the image name, and anyadditional parameters to be supplied to the process. When rules areconfigured, the values for the configuration parameters of Action 83 maybe overridden and replaced with the values of the Condition State 80variables of conditions contained in the currently configured rule. Anexample of this is shown in FIG. 10, 94 with the subject line beingdynamically generated from a format string and the “deviceName” statevariable of a referenced condition of the rule.

Once the Rules Engine 85 (FIG. 9) determines the currently configuredset of Rules 84, it creates instances of Condition objects. Conditionobjects are supplied with any configured input parameters when they arecreated as well as interfaces to acquire the inputs for Historical TrendValues 86, Instant Values 87, and Environmental Values 88. In oneexemplary embodiment of the present invention, the interface forHistorical Trend. Values 86 represents an interface to the historicalDatabase 7. In another exemplary embodiment of the present invention,the interface for Historical Trend Values 86 represents in interface tothe local data storage for data that has not yet been sent by the MobileDevice 1 to the Web Server 6. In yet another exemplary embodiment, theinterface for Historical Trend Values 86 may be null. A null interfacewould mean that those input values arc disabled and any artificialintelligence rule that requires such inputs will result in error. Thismay be useful when the Artificial Intelligence engine 8 is on the MobileDevice 1 and network usage concerns preclude using database input inCondition and Predicate evaluation, Instant values may be derived fromthe current data collection cycle on the Mobile Device 1 or fromincoming messages from Mobile Devices 1 on the Web Server 6.Environmental Conditions may be derived from the system data collector15, incoming messages from Mobile Devices 1 on the Web Server, ordirectly from the operating system itself.

Then, on the configured interval, the Rules Engine 85 will iteratethrough the configured Rules 84 requesting that each one evaluate allcontained Predicates 89 and Conditions 82. The Rules Engine 85 thenproceeds to fire any Actions 83 when the Conditions 82 and Predicates 89evaluate to true or when the rule is stateful and the Conditions 82 andPredicates 89 evaluate back to false after having previously evaluatedto true.

The Artificial Intelligence engine 8, 19 has the ability to interactwith other systems through some of its Actions 83 to create additionalvalue. For example, the ModifyOS and Launch Actions 83 can both be usedto interact with systems as described in, e.g., U.S. Pat. Nos,7,778,260, 7,602,782, 7,574,208, 7,346,370, 7,136,645, 6,981,047,6,826,405, 6,418,324, 6,347,340, 6,198,920, 6,193,152, U.S. PatentApplication Publication Nos. US2010/0046436, US2009/0307522,US2009/0083835, US2007/0206591, US2006/0203804, US2006/0187956,US2006/0146825, US20060046716, US2006/0023676, US2006/0009213,US2005/0237982, US2005/0002419, US2004/0264402, US2004/0170181,US2003/0017845, US2005/0223115, US2005/0223114 US2003/0120811, andUS2002/0122394, the disclosures of which are expressly incorporated byreference herein in their entireties. In this manner, users can beallowed to dynamically and automatically control the behavior of theirMobile VPN and Policy Management systems according to instant datameasurements, data measurement trends, and environmental conditions.

All of the data measurements that are collected by the data collectionagents 9 on the Mobile Devices 1 are periodically sent to the Web Server6 to be stored in the Database 7. The Database 7 stores all of thehistorical data measurements in a correlated data model. One exemplaryembodiment of the correlated data model is shown in FIG. 8. The presentinvention makes all of the historical data measurements stored in theDatabase 7 available for analysis through a series of businessintelligence reports.

Business intelligence reports are provided to analyze business concerns.By way of non-limiting example, some of the business concerns that canbe addressed by applying analysis to the correlated data capture fromdata collection agents on Mobile Devices 1 in the present inventioninclude monitoring the actual and predicted cost of public network useby a population of Mobile Devices 1, analyzing the productivity of apopulation of Mobile Devices 1, and managing the inventory of MobileDevices 1 and their associated network interface devices. Additionalnon-limiting examples include geographical maps with overlays of thecollected data. By way of non-limiting example, the present inventioncan provide overlays of the locations of Mobile Devices 1 over time andthe reported signal strength and other performance measures of publicnetworks over space and time. In addition, the present inventionprovides for data correlation between the geographical maps and thebusiness intelligence reports such that each may be filtered by the dataelements comprising the other. In accordance with embodiments, variousbusiness intelligence tools, such as those available from QlikTech(QlikView) and Microsoft (BING Maps) can be utilized in their‘off-the-shelf’ state to plot, display and analyze the data collected bythe participating Mobile Devices 1 of the disclosed system and method.Further, this information can be combined in accordance with anexemplary embodiment to interchange filtering information therebyenhancing the applicability and value of the displayed information.

By way of non-limiting, example, FIG. 14 shows an exemplary businessintelligence report to analyze public wireless network. plan use among apopulation of Mobile Devices 1. In this report, Filters 100 allow thereports to show only information that pertains to particular devices,users, phone numbers, applications, among others. Chart 101 shows MobileDevices 1 that have under-utilized their public wireless network plan.Chart 102 shows a distribution of network. use across all public networkcarriers in use by the Mobile Device 1 population. Charts 103 and 104both show the amount of network use by Mobile Device 1 while using anetwork interface that was roaming and possibly incurring additionalcharges. Chart 105 shows actual and projected data usage by MobileDevice 1. And Chart 106 shows the amount of network errors encounteredby the population of Mobile Devices 1.

Also by way of non-limiting example, FIG. 15 shows an exemplary businessintelligence report to analyze the productivity of a population ofMobile Devices 1. In this report, Filters 110 allow the reports to showonly information that pertains to particular devices, users, phonenumbers, applications, among others. Chart 111 shows the applicationsthat were most in use. Chart 112 shows the amount of network errors thatare occurring by carrier. Chart 113 shows the trend over time of networktechnology use by the population of Mobile Devices 1. And Chart 114shows the amount of compression that the Mobile Devices are experiencingwith their VPN provider.

Also by way of non-limiting example, FIG. 16 shows an exemplary businessintelligence report to manage the equipment inventory for a mobiledevice population. In this report, Filters 120 allow the reports to showonly information that pertains to particular devices, users, phonenumbers, applications, among others. This report is a table with Columnsfor Device Name 128, User Name 127 Phone Number 126, Carrier 125,Manufacturer 124, Model 123, Operating System 122, and Last UsedTimestamp 121.

Also by way of non-limiting example, FIG. 17 shows an exemplarygeographical report with data overlay showing information like carriername and signal strength over space and time. Filters 130 allow thereports to show only information that pertains to particular devices,users, phone numbers, applications, among others. The performance datais plotted over the geographical map as indicated in 131.

Also by way of non-limiting example, FIG. 18 shows an exemplarygeographical report with data overlay showing the location of a MobileDevice over time. Filters 140 allow the reports to show only informationthat pertains to particular devices, users, phone numbers, applications,among others. The device location as well as the network performanceexperienced by the device along its route is plotted over thegeographical map as indicated in 141.

Although the invention has been described with reference to severalexemplary embodiments, it is understood that the words that have beenused are words of description and illustration, rather than words oflimitation. Changes may be made within the purview of the appendedclaims, as presently stated and as amended, without departing from thescope and spirit of the invention in its aspects. Although the inventionhas been described with reference to particular means, materials andembodiments, the invention is not intended to be limited to theparticulars disclosed; rather, the invention extends to all functionallyequivalent structures, methods, and uses such as are within the scope ofthe appended claims.

In accordance with various embodiments of the present invention, themethods described herein are intended for operation as software programsrunning on a computer processor. Dedicated hardware implementationsincluding, but not limited to, application specific integrated circuits,programmable logic arrays and other hardware devices can likewise beconstructed to implement the methods described herein, Furthermore,alternative software implementations including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods described herein.

It should also be noted that the software implementations of the presentinvention as described herein are optionally stored on a tangiblestorage medium, such as: a magnetic medium such as a disk or tape; amagneto-optical or optical medium such as a disk; or a solid statemedium such as a memory card or other package that houses one or moreread-only (non-volatile) memories, random access memories, or otherre-writable (volatile) memories. A digital file attachment to email orother self-contained information archive or set of archives isconsidered a distribution medium equivalent to a tangible storagemedium. Accordingly, the invention is considered to include a tangiblestorage medium or distribution medium, as listed herein and includingart-recognized equivalents and successor media, in which the softwareimplementations herein are stored.

Although the present specification describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the invention is not limited to such standards andprotocols. Each of the standards for Internet and other packet switchednetwork transmission and wireless networking represent examples of thestate of the art. Such standards are periodically superseded by fasteror more efficient equivalents having essentially the same functions.Accordingly, replacement standards and protocols having the samefunctions are considered equivalents.

Moreover, it is noted that the foregoing examples have been providedmerely for the purpose of explanation and are in no way to be construedas limiting of the present invention. While the present invention hasbeen described with reference to an exemplary embodiment, it isunderstood that the words which have been used herein are words ofdescription and illustration, rather than words of limitation. Changesmay be made, within the purview of the appended claims, as presentlystated and as amended, without departing from the scope and spirit ofthe present invention in its aspects. Although the present invention hasbeen described herein with reference to particular means, materials andembodiments, the present invention is not intended to be limited to theparticulars disclosed herein; rather, the present invention extends toall functionally equivalent structures, methods and uses, such as arewithin the scope of the appended claims.

What is claimed:
 1. A method for managing performance in acommunications environment comprising multiple different networks thatinclude public and/or private data networks, the method comprising:receiving from a plurality of mobile devices arranged as networkterminal nodes located at respective current locations within thecommunications environment, data specific to each network interface ofeach of the plurality of mobile devices, which was simultaneouslycollected by each of the plurality of mobile devices at the respectivecurrent locations, the data being, related to at least one of servicecoverage; service quality; and usage of the public and/or private datanetworks for enterprise clients; and graphically displaying thecollected data from the multiple different networks to at least one oftrack, troubleshoot, and analyze the one of the service coverage; theservice quality; and the usage of the public and/or private datanetworks for the enterprise clients at the respective current locations.